Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations189516
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory82.9 MiB
Average record size in memory458.7 B

Variable types

Numeric10
Categorical6
Text1

Alerts

Cluster_DBSCAN is highly overall correlated with add_to_cart_order and 1 other fieldsHigh correlation
add_to_cart_order is highly overall correlated with Cluster_DBSCANHigh correlation
day is highly overall correlated with order_dowHigh correlation
department is highly overall correlated with department_idHigh correlation
department_id is highly overall correlated with departmentHigh correlation
max_order is highly overall correlated with order_number and 1 other fieldsHigh correlation
order_dow is highly overall correlated with dayHigh correlation
order_hour_of_day is highly overall correlated with order_time_listHigh correlation
order_number is highly overall correlated with max_order and 1 other fieldsHigh correlation
order_number_group is highly overall correlated with max_order and 1 other fieldsHigh correlation
order_time_list is highly overall correlated with order_hour_of_dayHigh correlation
reordered is highly overall correlated with Cluster_DBSCANHigh correlation
order_dow has 36565 (19.3%) zeros Zeros
days_since_prior_order has 2723 (1.4%) zeros Zeros

Reproduction

Analysis started2024-11-07 01:03:50.134226
Analysis finished2024-11-07 01:04:06.882990
Duration16.75 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

order_id
Real number (ℝ)

Distinct106346
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1707147.1
Minimum10
Maximum3420991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-11-06T20:04:06.952996image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile173219.75
Q1850455.5
median1706160
Q32558574.8
95-th percentile3243192
Maximum3420991
Range3420981
Interquartile range (IQR)1708119.2

Descriptive statistics

Standard deviation985588.47
Coefficient of variation (CV)0.57733074
Kurtosis-1.199088
Mean1707147.1
Median Absolute Deviation (MAD)853927
Skewness0.0034271321
Sum3.2353168 × 1011
Variance9.7138463 × 1011
MonotonicityNot monotonic
2024-11-06T20:04:07.037327image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45973 15
 
< 0.1%
790903 14
 
< 0.1%
2869702 12
 
< 0.1%
2988851 12
 
< 0.1%
2621625 11
 
< 0.1%
2352182 11
 
< 0.1%
432719 11
 
< 0.1%
1019050 11
 
< 0.1%
293169 10
 
< 0.1%
3269518 10
 
< 0.1%
Other values (106336) 189399
99.9%
ValueCountFrequency (%)
10 2
< 0.1%
11 1
 
< 0.1%
38 1
 
< 0.1%
56 1
 
< 0.1%
64 1
 
< 0.1%
147 2
< 0.1%
227 1
 
< 0.1%
243 1
 
< 0.1%
372 1
 
< 0.1%
471 3
< 0.1%
ValueCountFrequency (%)
3420991 3
< 0.1%
3420952 1
 
< 0.1%
3420910 2
< 0.1%
3420899 1
 
< 0.1%
3420898 1
 
< 0.1%
3420861 2
< 0.1%
3420771 4
< 0.1%
3420760 3
< 0.1%
3420584 2
< 0.1%
3420542 2
< 0.1%

user_id
Real number (ℝ)

Distinct68153
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103102.85
Minimum2
Maximum206209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-11-06T20:04:07.135285image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10750
Q151690
median102860.5
Q3154590.5
95-th percentile195822
Maximum206209
Range206207
Interquartile range (IQR)102900.5

Descriptive statistics

Standard deviation59479.667
Coefficient of variation (CV)0.57689647
Kurtosis-1.2034919
Mean103102.85
Median Absolute Deviation (MAD)51467.5
Skewness0.0061718094
Sum1.9539639 × 1010
Variance3.5378308 × 109
MonotonicityNot monotonic
2024-11-06T20:04:07.292969image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
176478 52
 
< 0.1%
126305 41
 
< 0.1%
129928 40
 
< 0.1%
115495 36
 
< 0.1%
31903 34
 
< 0.1%
97816 28
 
< 0.1%
47076 28
 
< 0.1%
15503 28
 
< 0.1%
203166 28
 
< 0.1%
28453 27
 
< 0.1%
Other values (68143) 189174
99.8%
ValueCountFrequency (%)
2 3
< 0.1%
3 1
 
< 0.1%
7 7
< 0.1%
10 3
< 0.1%
13 3
< 0.1%
17 1
 
< 0.1%
21 1
 
< 0.1%
22 2
 
< 0.1%
31 1
 
< 0.1%
35 3
< 0.1%
ValueCountFrequency (%)
206209 5
< 0.1%
206208 2
 
< 0.1%
206206 1
 
< 0.1%
206204 1
 
< 0.1%
206201 3
< 0.1%
206197 1
 
< 0.1%
206193 1
 
< 0.1%
206189 1
 
< 0.1%
206187 1
 
< 0.1%
206186 1
 
< 0.1%

order_number
Real number (ℝ)

High correlation 

Distinct99
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.203893
Minimum2
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-11-06T20:04:07.410328image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q16
median12
Q325
95-th percentile55
Maximum100
Range98
Interquartile range (IQR)19

Descriptive statistics

Standard deviation17.555727
Coefficient of variation (CV)0.96439412
Kurtosis3.1766306
Mean18.203893
Median Absolute Deviation (MAD)8
Skewness1.7399547
Sum3449929
Variance308.20356
MonotonicityNot monotonic
2024-11-06T20:04:07.553316image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 12272
 
6.5%
3 12133
 
6.4%
4 11880
 
6.3%
5 10556
 
5.6%
6 9298
 
4.9%
7 8560
 
4.5%
8 7577
 
4.0%
9 7052
 
3.7%
10 6521
 
3.4%
11 6266
 
3.3%
Other values (89) 97401
51.4%
ValueCountFrequency (%)
2 12272
6.5%
3 12133
6.4%
4 11880
6.3%
5 10556
5.6%
6 9298
4.9%
7 8560
4.5%
8 7577
4.0%
9 7052
3.7%
10 6521
3.4%
11 6266
3.3%
ValueCountFrequency (%)
100 53
< 0.1%
99 47
 
< 0.1%
98 79
< 0.1%
97 92
< 0.1%
96 108
0.1%
95 71
< 0.1%
94 91
< 0.1%
93 97
0.1%
92 123
0.1%
91 81
< 0.1%

order_dow
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7382279
Minimum0
Maximum6
Zeros36565
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-11-06T20:04:07.625653image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0909175
Coefficient of variation (CV)0.76360245
Kurtosis-1.3354969
Mean2.7382279
Median Absolute Deviation (MAD)2
Skewness0.17783666
Sum518938
Variance4.3719362
MonotonicityNot monotonic
2024-11-06T20:04:07.698496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 36565
19.3%
1 32668
17.2%
6 26156
13.8%
5 24908
13.1%
2 24802
13.1%
3 22478
11.9%
4 21939
11.6%
ValueCountFrequency (%)
0 36565
19.3%
1 32668
17.2%
2 24802
13.1%
3 22478
11.9%
4 21939
11.6%
5 24908
13.1%
6 26156
13.8%
ValueCountFrequency (%)
6 26156
13.8%
5 24908
13.1%
4 21939
11.6%
3 22478
11.9%
2 24802
13.1%
1 32668
17.2%
0 36565
19.3%

order_hour_of_day
Real number (ℝ)

High correlation 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.439678
Minimum0
Maximum23
Zeros1271
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-11-06T20:04:07.807511image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q110
median13
Q316
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.2434462
Coefficient of variation (CV)0.31574017
Kurtosis-0.020741059
Mean13.439678
Median Absolute Deviation (MAD)3
Skewness-0.037719742
Sum2547034
Variance18.006835
MonotonicityNot monotonic
2024-11-06T20:04:07.921328image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
10 16215
 
8.6%
11 16028
 
8.5%
14 15840
 
8.4%
15 15756
 
8.3%
13 15513
 
8.2%
12 15286
 
8.1%
16 14704
 
7.8%
9 14307
 
7.5%
17 12100
 
6.4%
8 9889
 
5.2%
Other values (14) 43878
23.2%
ValueCountFrequency (%)
0 1271
 
0.7%
1 663
 
0.3%
2 349
 
0.2%
3 284
 
0.1%
4 279
 
0.1%
5 566
 
0.3%
6 1752
 
0.9%
7 5184
 
2.7%
8 9889
5.2%
9 14307
7.5%
ValueCountFrequency (%)
23 2356
 
1.2%
22 3751
 
2.0%
21 4661
 
2.5%
20 5991
 
3.2%
19 7288
3.8%
18 9483
5.0%
17 12100
6.4%
16 14704
7.8%
15 15756
8.3%
14 15840
8.4%

days_since_prior_order
Real number (ℝ)

Zeros 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.420603
Minimum0
Maximum30
Zeros2723
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-11-06T20:04:08.005518image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q315
95-th percentile30
Maximum30
Range30
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.9802529
Coefficient of variation (CV)0.78632038
Kurtosis-0.24566243
Mean11.420603
Median Absolute Deviation (MAD)4
Skewness0.99706993
Sum2164387
Variance80.644943
MonotonicityNot monotonic
2024-11-06T20:04:08.073974image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
7 21367
 
11.3%
30 21249
 
11.2%
6 15625
 
8.2%
5 12835
 
6.8%
4 12417
 
6.6%
8 11970
 
6.3%
3 11293
 
6.0%
2 8728
 
4.6%
9 7591
 
4.0%
14 6430
 
3.4%
Other values (21) 60011
31.7%
ValueCountFrequency (%)
0 2723
 
1.4%
1 5930
 
3.1%
2 8728
4.6%
3 11293
6.0%
4 12417
6.6%
5 12835
6.8%
6 15625
8.2%
7 21367
11.3%
8 11970
6.3%
9 7591
 
4.0%
ValueCountFrequency (%)
30 21249
11.2%
29 1138
 
0.6%
28 1701
 
0.9%
27 1319
 
0.7%
26 1100
 
0.6%
25 1155
 
0.6%
24 1295
 
0.7%
23 1380
 
0.7%
22 2002
 
1.1%
21 2843
 
1.5%

product_id
Real number (ℝ)

Distinct134
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.096145
Minimum1
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-11-06T20:04:08.216835image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16
Q131
median83
Q3107
95-th percentile123
Maximum134
Range133
Interquartile range (IQR)76

Descriptive statistics

Standard deviation38.210891
Coefficient of variation (CV)0.53745377
Kurtosis-1.3246676
Mean71.096145
Median Absolute Deviation (MAD)33
Skewness-0.16461665
Sum13473857
Variance1460.0722
MonotonicityNot monotonic
2024-11-06T20:04:08.343055image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 21490
 
11.3%
83 19909
 
10.5%
123 10208
 
5.4%
120 8567
 
4.5%
21 5792
 
3.1%
84 5375
 
2.8%
115 4911
 
2.6%
107 4172
 
2.2%
91 3658
 
1.9%
112 3364
 
1.8%
Other values (124) 102070
53.9%
ValueCountFrequency (%)
1 413
 
0.2%
2 472
 
0.2%
3 2832
1.5%
4 1160
0.6%
5 363
 
0.2%
6 209
 
0.1%
7 192
 
0.1%
8 197
 
0.1%
9 1261
0.7%
10 52
 
< 0.1%
ValueCountFrequency (%)
134 53
 
< 0.1%
133 104
 
0.1%
132 35
 
< 0.1%
131 1563
0.8%
130 942
0.5%
129 1105
0.6%
128 1129
0.6%
127 245
 
0.1%
126 101
 
0.1%
125 236
 
0.1%

add_to_cart_order
Real number (ℝ)

High correlation 

Distinct84
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3662857
Minimum1
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-11-06T20:04:08.475810image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q311
95-th percentile22
Maximum135
Range134
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.159101
Coefficient of variation (CV)0.8557084
Kurtosis6.4088486
Mean8.3662857
Median Absolute Deviation (MAD)4
Skewness1.8695087
Sum1585545
Variance51.252727
MonotonicityNot monotonic
2024-11-06T20:04:08.598225image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 18691
 
9.9%
2 17982
 
9.5%
3 16621
 
8.8%
4 15642
 
8.3%
5 14338
 
7.6%
6 12851
 
6.8%
7 11585
 
6.1%
8 10307
 
5.4%
9 9222
 
4.9%
10 7925
 
4.2%
Other values (74) 54352
28.7%
ValueCountFrequency (%)
1 18691
9.9%
2 17982
9.5%
3 16621
8.8%
4 15642
8.3%
5 14338
7.6%
6 12851
6.8%
7 11585
6.1%
8 10307
5.4%
9 9222
4.9%
10 7925
4.2%
ValueCountFrequency (%)
135 1
< 0.1%
120 1
< 0.1%
93 2
< 0.1%
91 1
< 0.1%
90 2
< 0.1%
87 1
< 0.1%
85 1
< 0.1%
84 1
< 0.1%
78 2
< 0.1%
77 2
< 0.1%

reordered
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.0 MiB
1
119155 
0
70361 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters189516
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 119155
62.9%
0 70361
37.1%

Length

2024-11-06T20:04:08.682885image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T20:04:08.778309image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 119155
62.9%
0 70361
37.1%

Most occurring characters

ValueCountFrequency (%)
1 119155
62.9%
0 70361
37.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 189516
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 119155
62.9%
0 70361
37.1%

Most occurring scripts

ValueCountFrequency (%)
Common 189516
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 119155
62.9%
0 70361
37.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 189516
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 119155
62.9%
0 70361
37.1%

department_id
Real number (ℝ)

High correlation 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9318158
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-11-06T20:04:08.851673image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median9
Q316
95-th percentile19
Maximum21
Range20
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.2830562
Coefficient of variation (CV)0.63261909
Kurtosis-1.5613321
Mean9.9318158
Median Absolute Deviation (MAD)5
Skewness0.14969704
Sum1882238
Variance39.476796
MonotonicityNot monotonic
2024-11-06T20:04:09.363532image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4 55392
29.2%
16 31697
16.7%
19 16986
 
9.0%
7 15810
 
8.3%
1 13005
 
6.9%
13 10880
 
5.7%
3 6843
 
3.6%
15 6184
 
3.3%
20 6093
 
3.2%
9 5048
 
2.7%
Other values (11) 21578
 
11.4%
ValueCountFrequency (%)
1 13005
 
6.9%
2 209
 
0.1%
3 6843
 
3.6%
4 55392
29.2%
5 866
 
0.5%
6 1553
 
0.8%
7 15810
 
8.3%
8 562
 
0.3%
9 5048
 
2.7%
10 204
 
0.1%
ValueCountFrequency (%)
21 461
 
0.2%
20 6093
 
3.2%
19 16986
9.0%
18 2463
 
1.3%
17 4324
 
2.3%
16 31697
16.7%
15 6184
 
3.3%
14 4173
 
2.2%
13 10880
 
5.7%
12 4148
 
2.2%

department
Categorical

High correlation 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.2 MiB
produce
55392 
dairy eggs
31697 
snacks
16986 
beverages
15810 
frozen
13005 
Other values (16)
56626 

Length

Max length15
Median length13
Mean length8.0003641
Min length4

Characters and Unicode

Total characters1516197
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdairy eggs
2nd rowcanned goods
3rd rowcanned goods
4th rowproduce
5th rowbakery

Common Values

ValueCountFrequency (%)
produce 55392
29.2%
dairy eggs 31697
16.7%
snacks 16986
 
9.0%
beverages 15810
 
8.3%
frozen 13005
 
6.9%
pantry 10880
 
5.7%
bakery 6843
 
3.6%
canned goods 6184
 
3.3%
deli 6093
 
3.2%
dry goods pasta 5048
 
2.7%
Other values (11) 21578
 
11.4%

Length

2024-11-06T20:04:09.518990image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
produce 55392
22.7%
dairy 31697
13.0%
eggs 31697
13.0%
snacks 16986
 
7.0%
beverages 15810
 
6.5%
frozen 13005
 
5.3%
goods 11232
 
4.6%
pantry 10880
 
4.5%
bakery 6843
 
2.8%
canned 6184
 
2.5%
Other values (16) 44530
18.2%

Most occurring characters

ValueCountFrequency (%)
e 193454
12.8%
r 149840
 
9.9%
a 126803
 
8.4%
d 124118
 
8.2%
s 116966
 
7.7%
o 113914
 
7.5%
g 90897
 
6.0%
c 82043
 
5.4%
p 74497
 
4.9%
n 60974
 
4.0%
Other values (13) 382691
25.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1461457
96.4%
Space Separator 54740
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 193454
13.2%
r 149840
10.3%
a 126803
 
8.7%
d 124118
 
8.5%
s 116966
 
8.0%
o 113914
 
7.8%
g 90897
 
6.2%
c 82043
 
5.6%
p 74497
 
5.1%
n 60974
 
4.2%
Other values (12) 327951
22.4%
Space Separator
ValueCountFrequency (%)
54740
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1461457
96.4%
Common 54740
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 193454
13.2%
r 149840
10.3%
a 126803
 
8.7%
d 124118
 
8.5%
s 116966
 
8.0%
o 113914
 
7.8%
g 90897
 
6.2%
c 82043
 
5.6%
p 74497
 
5.1%
n 60974
 
4.2%
Other values (12) 327951
22.4%
Common
ValueCountFrequency (%)
54740
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1516197
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 193454
12.8%
r 149840
 
9.9%
a 126803
 
8.4%
d 124118
 
8.2%
s 116966
 
7.7%
o 113914
 
7.5%
g 90897
 
6.0%
c 82043
 
5.4%
p 74497
 
4.9%
n 60974
 
4.0%
Other values (13) 382691
25.2%
Distinct134
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.4 MiB
2024-11-06T20:04:09.717882image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length29
Median length23
Mean length14.420223
Min length3

Characters and Unicode

Total characters2732863
Distinct characters26
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowspecialty cheeses
2nd rowcanned jarred vegetables
3rd rowsoup broth bouillon
4th rowfresh fruits
5th rowbreakfast bakery
ValueCountFrequency (%)
fresh 45870
 
11.4%
vegetables 31934
 
7.9%
fruits 31794
 
7.9%
packaged 18612
 
4.6%
frozen 10068
 
2.5%
water 9822
 
2.4%
yogurt 8567
 
2.1%
ice 5941
 
1.5%
cheese 5792
 
1.4%
milk 5375
 
1.3%
Other values (194) 228628
56.8%
2024-11-06T20:04:10.157104image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 389355
14.2%
s 258706
 
9.5%
r 232201
 
8.5%
a 214506
 
7.8%
212887
 
7.8%
t 160486
 
5.9%
f 118125
 
4.3%
i 109651
 
4.0%
o 102984
 
3.8%
g 102195
 
3.7%
Other values (16) 831767
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2519976
92.2%
Space Separator 212887
 
7.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 389355
15.5%
s 258706
 
10.3%
r 232201
 
9.2%
a 214506
 
8.5%
t 160486
 
6.4%
f 118125
 
4.7%
i 109651
 
4.4%
o 102984
 
4.1%
g 102195
 
4.1%
c 97165
 
3.9%
Other values (15) 734602
29.2%
Space Separator
ValueCountFrequency (%)
212887
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2519976
92.2%
Common 212887
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 389355
15.5%
s 258706
 
10.3%
r 232201
 
9.2%
a 214506
 
8.5%
t 160486
 
6.4%
f 118125
 
4.7%
i 109651
 
4.4%
o 102984
 
4.1%
g 102195
 
4.1%
c 97165
 
3.9%
Other values (15) 734602
29.2%
Common
ValueCountFrequency (%)
212887
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2732863
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 389355
14.2%
s 258706
 
9.5%
r 232201
 
8.5%
a 214506
 
7.8%
212887
 
7.8%
t 160486
 
5.9%
f 118125
 
4.3%
i 109651
 
4.0%
o 102984
 
3.8%
g 102195
 
3.7%
Other values (16) 831767
30.4%

day
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.1 MiB
Monday
36565 
Tuesday
32668 
Sunday
26156 
Saturday
24908 
Wednesday
24802 
Other values (2)
44417 

Length

Max length9
Median length8
Mean length7.0650605
Min length6

Characters and Unicode

Total characters1338942
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWednesday
2nd rowThursday
3rd rowFriday
4th rowFriday
5th rowWednesday

Common Values

ValueCountFrequency (%)
Monday 36565
19.3%
Tuesday 32668
17.2%
Sunday 26156
13.8%
Saturday 24908
13.1%
Wednesday 24802
13.1%
Thursday 22478
11.9%
Friday 21939
11.6%

Length

2024-11-06T20:04:10.305874image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T20:04:10.474533image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
monday 36565
19.3%
tuesday 32668
17.2%
sunday 26156
13.8%
saturday 24908
13.1%
wednesday 24802
13.1%
thursday 22478
11.9%
friday 21939
11.6%

Most occurring characters

ValueCountFrequency (%)
a 214424
16.0%
d 214318
16.0%
y 189516
14.2%
u 106210
7.9%
n 87523
6.5%
e 82272
 
6.1%
s 79948
 
6.0%
r 69325
 
5.2%
T 55146
 
4.1%
S 51064
 
3.8%
Other values (7) 189196
14.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1149426
85.8%
Uppercase Letter 189516
 
14.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 214424
18.7%
d 214318
18.6%
y 189516
16.5%
u 106210
9.2%
n 87523
7.6%
e 82272
 
7.2%
s 79948
 
7.0%
r 69325
 
6.0%
o 36565
 
3.2%
t 24908
 
2.2%
Other values (2) 44417
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
T 55146
29.1%
S 51064
26.9%
M 36565
19.3%
W 24802
13.1%
F 21939
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 1338942
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 214424
16.0%
d 214318
16.0%
y 189516
14.2%
u 106210
7.9%
n 87523
6.5%
e 82272
 
6.1%
s 79948
 
6.0%
r 69325
 
5.2%
T 55146
 
4.1%
S 51064
 
3.8%
Other values (7) 189196
14.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1338942
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 214424
16.0%
d 214318
16.0%
y 189516
14.2%
u 106210
7.9%
n 87523
6.5%
e 82272
 
6.1%
s 79948
 
6.0%
r 69325
 
5.2%
T 55146
 
4.1%
S 51064
 
3.8%
Other values (7) 189196
14.1%

order_time_list
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.1 MiB
Morning
78661 
Afternoon
73913 
Night
31174 
Dawn
 
5768

Length

Max length9
Median length7
Mean length7.3597269
Min length4

Characters and Unicode

Total characters1394786
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfternoon
2nd rowAfternoon
3rd rowNight
4th rowMorning
5th rowMorning

Common Values

ValueCountFrequency (%)
Morning 78661
41.5%
Afternoon 73913
39.0%
Night 31174
 
16.4%
Dawn 5768
 
3.0%

Length

2024-11-06T20:04:10.657905image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T20:04:10.754104image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
morning 78661
41.5%
afternoon 73913
39.0%
night 31174
 
16.4%
dawn 5768
 
3.0%

Most occurring characters

ValueCountFrequency (%)
n 310916
22.3%
o 226487
16.2%
r 152574
10.9%
i 109835
 
7.9%
g 109835
 
7.9%
t 105087
 
7.5%
M 78661
 
5.6%
A 73913
 
5.3%
f 73913
 
5.3%
e 73913
 
5.3%
Other values (5) 79652
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1205270
86.4%
Uppercase Letter 189516
 
13.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 310916
25.8%
o 226487
18.8%
r 152574
12.7%
i 109835
 
9.1%
g 109835
 
9.1%
t 105087
 
8.7%
f 73913
 
6.1%
e 73913
 
6.1%
h 31174
 
2.6%
a 5768
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
M 78661
41.5%
A 73913
39.0%
N 31174
 
16.4%
D 5768
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1394786
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 310916
22.3%
o 226487
16.2%
r 152574
10.9%
i 109835
 
7.9%
g 109835
 
7.9%
t 105087
 
7.5%
M 78661
 
5.6%
A 73913
 
5.3%
f 73913
 
5.3%
e 73913
 
5.3%
Other values (5) 79652
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1394786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 310916
22.3%
o 226487
16.2%
r 152574
10.9%
i 109835
 
7.9%
g 109835
 
7.9%
t 105087
 
7.5%
M 78661
 
5.6%
A 73913
 
5.3%
f 73913
 
5.3%
e 73913
 
5.3%
Other values (5) 79652
 
5.7%

max_order
Real number (ℝ)

High correlation 

Distinct99
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.597153
Minimum2
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2024-11-06T20:04:10.923660image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q18
median19
Q339
95-th percentile78
Maximum100
Range98
Interquartile range (IQR)31

Descriptive statistics

Standard deviation23.092888
Coefficient of variation (CV)0.86824661
Kurtosis0.84836213
Mean26.597153
Median Absolute Deviation (MAD)13
Skewness1.2140408
Sum5040586
Variance533.28146
MonotonicityNot monotonic
2024-11-06T20:04:11.102530image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 7982
 
4.2%
3 7412
 
3.9%
5 7332
 
3.9%
2 6741
 
3.6%
6 6601
 
3.5%
7 6188
 
3.3%
8 5718
 
3.0%
9 5410
 
2.9%
10 5031
 
2.7%
11 5024
 
2.7%
Other values (89) 126077
66.5%
ValueCountFrequency (%)
2 6741
3.6%
3 7412
3.9%
4 7982
4.2%
5 7332
3.9%
6 6601
3.5%
7 6188
3.3%
8 5718
3.0%
9 5410
2.9%
10 5031
2.7%
11 5024
2.7%
ValueCountFrequency (%)
100 323
0.2%
99 468
0.2%
98 535
0.3%
97 431
0.2%
96 526
0.3%
95 361
0.2%
94 352
0.2%
93 418
0.2%
92 352
0.2%
91 302
0.2%

order_number_group
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.7 MiB
1-20 order
98479 
21-40 order
47166 
41-60 order
24558 
61-80 order
11103 
81-100 order
 
8210

Length

Max length12
Median length10
Mean length10.523687
Min length10

Characters and Unicode

Total characters1994407
Distinct characters12
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1-20 order
2nd row21-40 order
3rd row21-40 order
4th row41-60 order
5th row21-40 order

Common Values

ValueCountFrequency (%)
1-20 order 98479
52.0%
21-40 order 47166
24.9%
41-60 order 24558
 
13.0%
61-80 order 11103
 
5.9%
81-100 order 8210
 
4.3%

Length

2024-11-06T20:04:11.280138image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T20:04:11.442714image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
order 189516
50.0%
1-20 98479
26.0%
21-40 47166
 
12.4%
41-60 24558
 
6.5%
61-80 11103
 
2.9%
81-100 8210
 
2.2%

Most occurring characters

ValueCountFrequency (%)
r 379032
19.0%
1 197726
9.9%
0 197726
9.9%
- 189516
9.5%
189516
9.5%
o 189516
9.5%
d 189516
9.5%
e 189516
9.5%
2 145645
 
7.3%
4 71724
 
3.6%
Other values (2) 54974
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 947580
47.5%
Decimal Number 667795
33.5%
Dash Punctuation 189516
 
9.5%
Space Separator 189516
 
9.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 197726
29.6%
0 197726
29.6%
2 145645
21.8%
4 71724
 
10.7%
6 35661
 
5.3%
8 19313
 
2.9%
Lowercase Letter
ValueCountFrequency (%)
r 379032
40.0%
o 189516
20.0%
d 189516
20.0%
e 189516
20.0%
Dash Punctuation
ValueCountFrequency (%)
- 189516
100.0%
Space Separator
ValueCountFrequency (%)
189516
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1046827
52.5%
Latin 947580
47.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 197726
18.9%
0 197726
18.9%
- 189516
18.1%
189516
18.1%
2 145645
13.9%
4 71724
 
6.9%
6 35661
 
3.4%
8 19313
 
1.8%
Latin
ValueCountFrequency (%)
r 379032
40.0%
o 189516
20.0%
d 189516
20.0%
e 189516
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1994407
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 379032
19.0%
1 197726
9.9%
0 197726
9.9%
- 189516
9.5%
189516
9.5%
o 189516
9.5%
d 189516
9.5%
e 189516
9.5%
2 145645
 
7.3%
4 71724
 
3.6%
Other values (2) 54974
 
2.8%

Cluster_DBSCAN
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.0 MiB
0
119133 
1
70346 
-1
 
37

Length

Max length2
Median length1
Mean length1.0001952
Min length1

Characters and Unicode

Total characters189553
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 119133
62.9%
1 70346
37.1%
-1 37
 
< 0.1%

Length

2024-11-06T20:04:11.579955image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T20:04:11.707662image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 119133
62.9%
1 70383
37.1%

Most occurring characters

ValueCountFrequency (%)
0 119133
62.8%
1 70383
37.1%
- 37
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 189516
> 99.9%
Dash Punctuation 37
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 119133
62.9%
1 70383
37.1%
Dash Punctuation
ValueCountFrequency (%)
- 37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 189553
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 119133
62.8%
1 70383
37.1%
- 37
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 189553
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 119133
62.8%
1 70383
37.1%
- 37
 
< 0.1%

Interactions

2024-11-06T20:04:05.252552image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:56.060333image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:57.138191image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:58.112130image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:59.108242image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:00.145000image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:01.411674image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:02.493284image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:03.374999image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:04.286872image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:05.338264image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:56.193244image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:57.206916image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:58.264070image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:59.193833image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:00.261999image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:01.497319image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:02.604415image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:03.480878image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:04.370182image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:05.403966image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:56.260093image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:57.271023image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:58.389642image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:59.304616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:00.415536image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:01.563859image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:02.686764image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:03.592737image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:04.480166image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:05.478888image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:56.333949image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:57.344936image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:58.502917image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:59.380158image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:00.568794image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:01.635968image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:02.762830image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:03.672596image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:04.635411image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:05.568933image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:56.455231image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:57.488901image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:58.622908image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:59.452703image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:00.695232image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:01.707496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:02.899393image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:03.751236image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:04.739429image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:05.673931image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:56.565834image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:57.608041image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:58.695291image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:59.574430image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:00.790958image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:01.807072image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:02.993321image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:03.820003image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:04.812321image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:05.745770image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:56.634411image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:57.698786image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:58.765002image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:59.655660image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:00.909381image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:01.880809image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:03.066761image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:03.935313image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:04.896904image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:05.818982image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:56.712963image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:57.817130image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:58.842032image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:59.723439image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:01.064061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:01.958761image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:03.139091image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:04.047007image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:04.982645image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:05.890695image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:56.859905image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:57.883906image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:58.930706image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:59.843926image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:01.222121image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:02.250659image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:03.212591image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:04.135777image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:05.102082image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:06.007728image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:57.008055image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:57.975718image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:03:59.029115image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:00.027391image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:01.337085image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:02.388346image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:03.291171image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:04.211808image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-06T20:04:05.184328image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-11-06T20:04:11.831178image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Cluster_DBSCANadd_to_cart_orderdaydays_since_prior_orderdepartmentdepartment_idmax_orderorder_doworder_hour_of_dayorder_idorder_numberorder_number_grouporder_time_listproduct_idreordereduser_id
Cluster_DBSCAN1.0000.5130.0130.1000.1520.1200.1740.0130.0400.0060.1950.1530.0330.0691.0000.007
add_to_cart_order0.5131.0000.0150.0770.0340.015-0.009-0.016-0.016-0.004-0.0050.0140.0230.0040.1090.000
day0.0130.0151.0000.0540.0250.0200.0271.0000.0290.0070.0180.0310.0330.0110.0150.009
days_since_prior_order0.1000.0770.0541.0000.0190.003-0.493-0.042-0.006-0.001-0.3860.2660.0320.0040.1410.001
department0.1520.0340.0250.0191.0001.0000.0210.0250.0140.0020.0180.0300.0170.4380.2150.006
department_id0.1200.0150.0200.0031.0001.0000.0080.005-0.011-0.0040.0030.0220.0130.0220.169-0.000
max_order0.174-0.0090.027-0.4930.0210.0081.0000.020-0.0480.0020.7800.9750.0290.0010.246-0.006
order_dow0.013-0.0161.000-0.0420.0250.0050.0201.0000.0120.0040.0130.0310.0330.0000.0150.001
order_hour_of_day0.040-0.0160.029-0.0060.014-0.011-0.0480.0121.000-0.001-0.0380.0310.8360.0020.036-0.001
order_id0.006-0.0040.007-0.0010.002-0.0040.0020.004-0.0011.0000.0040.0060.0070.0000.0000.004
order_number0.195-0.0050.018-0.3860.0180.0030.7800.013-0.0380.0041.0000.5520.0250.0010.275-0.005
order_number_group0.1530.0140.0310.2660.0300.0220.9750.0310.0310.0060.5521.0000.0270.0200.2160.018
order_time_list0.0330.0230.0330.0320.0170.0130.0290.0330.8360.0070.0250.0271.0000.0090.0230.009
product_id0.0690.0040.0110.0040.4380.0220.0010.0000.0020.0000.0010.0200.0091.0000.0980.002
reordered1.0000.1090.0150.1410.2150.1690.2460.0150.0360.0000.2750.2160.0230.0981.0000.000
user_id0.0070.0000.0090.0010.006-0.000-0.0060.001-0.0010.004-0.0050.0180.0090.0020.0001.000

Missing values

2024-11-06T20:04:06.164950image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-06T20:04:06.468240image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

order_iduser_idorder_numberorder_doworder_hour_of_daydays_since_prior_orderproduct_idadd_to_cart_orderreordereddepartment_iddepartmentproduct_namedayorder_time_listmax_orderorder_number_groupCluster_DBSCAN
1083214700028169733142147.0212116dairy eggsspecialty cheesesWednesdayAfternoon141-20 order0
51829522231851993462631411.08114015canned goodscanned jarred vegetablesThursdayAfternoon2621-40 order1
112933325694126540264207.0696015canned goodssoup broth bouillonFridayNight2621-40 order1
5797311704510833874910.024314producefresh fruitsFridayMorning4741-60 order0
78054344895310868282810.093103bakerybreakfast bakeryWednesdayMorning4021-40 order1
10767471154159149142471143.0241014producefresh fruitsTuesdayAfternoon6361-80 order0
16296677203914891655104.01202116dairy eggsyogurtSaturdayMorning4641-60 order0
97972230406411925251158.0521811frozenfrozen breakfastTuesdayAfternoon51-20 order0
62193176562614737385127.0261517beveragescoffeeSaturdayMorning6461-80 order0
581783147906268668801615.0831714producefresh vegetablesMondayAfternoon131-20 order0
order_iduser_idorder_numberorder_doworder_hour_of_daydays_since_prior_orderproduct_idadd_to_cart_orderreordereddepartment_iddepartmentproduct_namedayorder_time_listmax_orderorder_number_groupCluster_DBSCAN
7719671100412152348211130.077817beveragessoft drinksTuesdayMorning21-20 order0
13196572992007113928201413.0216116dairy eggspackaged cheeseMondayAfternoon121-20 order0
10404853231589100011901611.024904producefresh fruitsMondayAfternoon91-20 order1
746486262124477692101.0112513bakerybreadWednesdayMorning10081-100 order0
60733191991852471761430.077907beveragessoft drinksSundayAfternoon171-20 order1
1660186256603496727335135.0162304producefresh herbsSaturdayAfternoon3321-40 order1
14268632845299177992241168.05911115canned goodscanned meals beansTuesdayAfternoon2421-40 order0
7957812448063119949585122.0781119snackscrackersSaturdayMorning5841-60 order0
1254879296396216931211594.0209011personal careoral hygieneSaturdayMorning111-20 order1
1758656242116022593150829.0831114producefresh vegetablesMondayMorning151-20 order0